Audio Data Analysis Using Deep Learning with Python (Part 1) - KDnuggets

While much of the literature and buzz on deep learning concerns computer vision and natural language processing(NLP), audio analysis -- a field that includes automatic speech recognition(ASR), digital signal processing, and music classification, tagging, and generation -- is a growing subdomain of deep learning applications. Some of the most popular and widespread machine learning systems, virtual assistants Alexa, Siri, and Google Home, are largely products built atop models that can extract information from audio signals. Audio data analysis is about analyzing and understanding audio signals captured by digital devices, with numerous applications in the enterprise, healthcare, productivity, and smart cities. Applications include customer satisfaction analysis from customer support calls, media content analysis and retrieval, medical diagnostic aids and patient monitoring, assistive technologies for people with hearing impairments, and audio analysis for public safety. In the first part of this article series, we will talk about all you need to know before getting started with the audio data analysis and extract necessary features from a sound/audio file. We will also build an Artificial Neural Network(ANN) for the music genre classification.